code: def observation(self, obs):
      # Debug output: max-x/y positions to watch exploration progress.
      if self.step_count == 0:
          for _ in range(self.framestack):
              self.frame_buffer.append(
                  np.zeros((self.single_frame_dim,))
              )
          if self.vector_index == 0:
              if self.x_positions:
                  max_diff = max(
                      np.sqrt(
                          (np.array(self.x_positions) - self.init_x)
                          ** 2
                          + (np.array(self.y_positions) - self.init_y)
                          ** 2
                      )
                  )
                  self.x_y_delta_buffer.append(max_diff)
                  print(
                      "100-average dist travelled={}".format(
                          np.mean(self.x_y_delta_buffer)
                      )
                  )
                  self.x_positions = []
                  self.y_positions = []
              self.init_x = self.agent_pos[0]
              self.init_y = self.agent_pos[1]
  
      # Are we carrying the key?
      # if self.carrying is not None:
      #    print("Carrying KEY!!")
  
      self.x_positions.append(self.agent_pos[0])
      self.y_positions.append(self.agent_pos[1])
  
      # One-hot the last dim into 11, 6, 3 one-hot vectors, then flatten.
      objects = one_hot(obs[:, :, 0], depth=11)
      colors = one_hot(obs[:, :, 1], depth=6)
      states = one_hot(obs[:, :, 2], depth=3)
      # Is the door we see open?
      # for x in range(7):
      #    for y in range(7):
      #        if objects[x, y, 4] == 1.0 and states[x, y, 0] == 1.0:
      #            print("Door OPEN!!")
  
      all_ = np.concatenate([objects, colors, states], -1)
      all_flat = np.reshape(all_, (-1,))
      direction = one_hot(np.array(self.agent_dir), depth=4).astype(
          np.float32
      )
      single_frame = np.concatenate([all_flat, direction])
      self.frame_buffer.append(single_frame)
      return np.concatenate(self.frame_buffer)
  
imports: from collections import deque
  import gymnasium
  import minigrid
  import numpy
  import sys
  import unittest
  import ray
  from ray import air, tune
  from ray.rllib.algorithms.callbacks import DefaultCallbacks
  import ray.rllib.algorithms.ppo
  from ray.rllib.utils.test_utils import check_learning_achieved, framework_iterator
  from ray.rllib.utils.numpy import one_hot
  from ray.tune import register_env
label_desc: Use image node_id 2 for calling the OneHotWrapper obj's underlying member method code with example usage: obj.observation(obs) and returns: np